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How to maintain harmful algal blooms under phosphate-limitation is still an open question in mesotrophic/eutrophic lakes. Little evidence for the importance of suspended particulate matter (SPM) in mediating phosphorus cycling and contributing to eutrophication has been generated for aquatic ecosystems, especially in coupled river-lake systems. In this study, we examined phosphorus transport and redistribution in a river-lake system in the Lake Erhai basin by establishing the relations between phosphorus distribution and phosphorus sorption behavior on SPM, and predicted how changes in the quality and quantity of SPM might influence phosphorus cycling by laboratory experiments and modeling. During the wet seasons, TP pool shifted from being dominated by total dissolved phosphorus (TDP) in the Miju River and estuary regions (73±5%) to being dominated by total particulate phosphorus (TPP) (74±11%) in Lake Erhai. The detritus-SPM in the Miju River as a P-sink and phytoplankton-SPM in Lake Erhai as a P-source buffered TDP levels during the wet seasons, which attributed to P activity and phytoplankton-POC of SPM. Increasing SPM concentrations could enhance the P-buffering. When C 0 ≤ 5 μmol/L and phytoplankton-SPM ≥ 16 mg/L, P release increased by 50%-300%; when C 0 ≥ 5 μmol/L and detritus-SPM ≥ 16 mg/L, P removal could exceed 30%. This study highlights two distinctive roles of SPM in regulating P cycling during transport from rivers to lakes. Especially the phytoplankton-SPM to buffer phosphate-limitation during algal blooms should not be ignored, which could provide theoretical references for the mechanism of continued algal blooms in mesotrophic lakes., (Copyright © 2022. Published by Elsevier Ltd.)

To establish the influence of dissolved organic phosphorus (DOP) from different sources on the water quality of Erhai Lake in August 2014, enzymatic hydrolysis technology was used to characterize enzymatically hydrolyzable phosphorus (EHP). The results indicate that the DOP represents a significant source of the bioavailable P in the lake water and its different sources of Erhai Lake, and the bioavailability of the P pool may be underestimated by up to 55% if DOP is ignored and only the soluble-reactive phosphorus (SRP) is considered. The significant differences in DOP characteristics from the different sources may be related to the origin of the DOP, regional pollution, and environmental factors. The DOP from the sediment porewater can act as an important source of DOP in the lake water based on its high DOP content and high DOP loads from the sediment release. In addition, the highest EHP loads from the sediment release can threaten the water quality of Erhai Lake and sustain the algal blooms when they occur. Therefore, for the protection of Erhai Lake, the contributions of not only the SRP but also the DOP to the bioavailable P pool should be considered, emphasizing the eutrophication risk caused by DOP from sediments, especially in the middle section of Erhai Lake. The effects of DOP from the inflowing rivers on the water quality should also not be ignored, due to its high bioavailability.

[Display omitted] • A novel and efficient deep learning-based clustering approach (NLU-DC) was proposed for lake-related studies. • Six evolution patterns of lake topics were identified from over 130,000 studies. • No emerging topic after 2000 provokes more integrated and multi-pattern studies. • NLU-DC is a powerful method for global meta -analysis of water-related research fields. As complicated microcosms, lakes have been attracting exponentially increasing attention, resulting in plentiful interdisciplinary academic publications of more than 105. It is thereby challengeable to explore the massive unstructured text information of publications to understand lake topics from the global- and centurial-scale perspectives. However, conventional bibliometrics suffer from the limitations of non-understanding the literature. A novel approach, Natural Language Understanding-Based Deep Clustering (NLU-DC) for large text clustering, was proposed in this study for global meta -analysis of evolution patterns for lake topics. The validated NLU-DC elevated the available keywords from 24% to 70%, correcting the statistical bias in the traditional evidence synthesis. Its high performance derives from the integration of a deep learning model, cosine distance, DBSCAN clustering and changing hyperparameters. This approach is of great accuracy and efficiency for large text datasets. This study thereby identified the centurial-scale topics related to lakes using large literature datasets covering >130,000 studies. The results showed that the topics became more and more abundant but were concentrated stably towards central ones. Six evolution patterns, consisting of fluctuating, emerging in 1970, emerging after 1970, trending-up, stable and trending-down patterns, were identified with generalized linear model (GLM). We found that, in recent twenty years, few emerging topics attract significant academic attention; and the dependency between topics is catching more attention than before. To prolong the lake studies, it is essential to strengthen the integrated studies on multi-pattern topics, in particular, over emerging and trending-down topics. Our study verified that the NLU-DC, consisting of state-of-the-art deep learning models in natural language processing and efficient clustering algorithm in machine learning, is a powerful method for global meta -analysis of water-related research fields and have huge potential to be applied in all fields of academical studies. [ABSTRACT FROM AUTHOR]